Week 7 & 8 Assignment - Python

Name: Kesav Adithya Venkidusamy
Course: DSC640 - Data Presentation and Visualization
Instructor: Catherine Williams

These two weeks we are going to be focused on heat maps, spatial charts, and contour charts and using various tools to create these visualizations. You must consolidate all the charts into ONE document with each chart labeled with the type of chart and technology - for example: Python - Bar Chart. Failure to label and consolidate the charts will resort in points being taken off or a 0 for the assignment.

Sample Datasets (click on the Downloads tab.)

You may also download them directly from this link: Exercise 4.2 Datasets (click the link to download a folder containing the datasets.)

You need to submit:
1 heat map, 1 spatial chart and 1 funnel or violin chart using Tableau or PowerBI
1 heat map, 1 spatial chart and 1 contour chart using Python
1 heat map, 1 spatial chart and 1 contour chart using R

1 heat map, 1 spatial chart and 1 contour chart using Python

## Importing libraries required for this exercise
import pandas as pd
import numpy as np
import squarify
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.express as px
import seaborn as sns
import matplotlib
import plotly.graph_objects as go

Read Input datasets

## Reading the costco data
costco_df = pd.read_csv('costcos-geocoded.csv')
costco_df.head()

	Address	City	State	Zip Code	Latitude	Longitude
0	1205 N. Memorial Parkway	Huntsville	Alabama	35801-5930	34.743095	-86.600955
1	3650 Galleria Circle	Hoover	Alabama	35244-2346	33.377649	-86.812420
2	8251 Eastchase Parkway	Montgomery	Alabama	36117	32.363889	-86.150884
3	5225 Commercial Boulevard	Juneau	Alaska	99801-7210	58.359200	-134.483000
4	330 West Dimond Blvd	Anchorage	Alaska	99515-1950	61.143266	-149.884217

## Reading ppg dataset
ppg2008_df = pd.read_csv('ppg2008.csv')
ppg2008_df.head()

	Name	G	MIN	PTS	FGM	FGA	FGP	FTM	FTA	FTP	...	3PA	3PP	ORB	DRB	TRB	AST	STL	BLK	TO	PF
0	Dwyane Wade	79	38.6	30.2	10.8	22.0	0.491	7.5	9.8	0.765	...	3.5	0.317	1.1	3.9	5.0	7.5	2.2	1.3	3.4	2.3
1	LeBron James	81	37.7	28.4	9.7	19.9	0.489	7.3	9.4	0.780	...	4.7	0.344	1.3	6.3	7.6	7.2	1.7	1.1	3.0	1.7
2	Kobe Bryant	82	36.2	26.8	9.8	20.9	0.467	5.9	6.9	0.856	...	4.1	0.351	1.1	4.1	5.2	4.9	1.5	0.5	2.6	2.3
3	Dirk Nowitzki	81	37.7	25.9	9.6	20.0	0.479	6.0	6.7	0.890	...	2.1	0.359	1.1	7.3	8.4	2.4	0.8	0.8	1.9	2.2
4	Danny Granger	67	36.2	25.8	8.5	19.1	0.447	6.0	6.9	0.878	...	6.7	0.404	0.7	4.4	5.1	2.7	1.0	1.4	2.5	3.1

5 rows × 21 columns

ppg2008_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 21 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    50 non-null     object 
 1   G       50 non-null     int64  
 2   MIN     50 non-null     float64
 3   PTS     50 non-null     float64
 4   FGM     50 non-null     float64
 5   FGA     50 non-null     float64
 6   FGP     50 non-null     float64
 7   FTM     50 non-null     float64
 8   FTA     50 non-null     float64
 9   FTP     50 non-null     float64
 10  3PM     50 non-null     float64
 11  3PA     50 non-null     float64
 12  3PP     50 non-null     float64
 13  ORB     50 non-null     float64
 14  DRB     50 non-null     float64
 15  TRB     50 non-null     float64
 16  AST     50 non-null     float64
 17  STL     50 non-null     float64
 18  BLK     50 non-null     float64
 19  TO      50 non-null     float64
 20  PF      50 non-null     float64
dtypes: float64(19), int64(1), object(1)
memory usage: 8.3+ KB

1. Python - Heat Map

Plotting Heat Map for ppg test result

ppg2008_sub_df = ppg2008_df.copy()
ppg2008_sub_df.set_index('Name', inplace = True)
ppg2008_sub_df.head()

	G	MIN	PTS	FGM	FGA	FGP	FTM	FTA	FTP	3PM	3PA	3PP	ORB	DRB	TRB	AST	STL	BLK	TO	PF
Name
Dwyane Wade	79	38.6	30.2	10.8	22.0	0.491	7.5	9.8	0.765	1.1	3.5	0.317	1.1	3.9	5.0	7.5	2.2	1.3	3.4	2.3
LeBron James	81	37.7	28.4	9.7	19.9	0.489	7.3	9.4	0.780	1.6	4.7	0.344	1.3	6.3	7.6	7.2	1.7	1.1	3.0	1.7
Kobe Bryant	82	36.2	26.8	9.8	20.9	0.467	5.9	6.9	0.856	1.4	4.1	0.351	1.1	4.1	5.2	4.9	1.5	0.5	2.6	2.3
Dirk Nowitzki	81	37.7	25.9	9.6	20.0	0.479	6.0	6.7	0.890	0.8	2.1	0.359	1.1	7.3	8.4	2.4	0.8	0.8	1.9	2.2
Danny Granger	67	36.2	25.8	8.5	19.1	0.447	6.0	6.9	0.878	2.7	6.7	0.404	0.7	4.4	5.1	2.7	1.0	1.4	2.5	3.1

# Normalize columns
ppg2008_sub_norm = (ppg2008_sub_df - ppg2008_sub_df.mean())/ppg2008_sub_df.std()

# Initialize the matplotlib figure
f, ax = plt.subplots(figsize=(13, 12))

# Create heatmap of normalized data
sns.heatmap(ppg2008_sub_norm, cmap='YlOrRd', cbar=False)

# Add chart title and labels
plt.title("Python - Heat Map to show NBA Per Game Performance", fontsize = 15, loc = 'left')
plt.ylabel("")

plt.show()

Python - Spatial Chart

fig = go.Figure(data=go.Scattergeo(
        locationmode = 'USA-states',
        lon = costco_df['Longitude'],
        lat = costco_df['Latitude'],
        text = costco_df['Address'],
        mode = 'markers',
        marker = dict(
            size = 8,
            opacity = 0.8,
            reversescale = True,
            autocolorscale = False,
            symbol = 'circle',
            line = dict(
                    width = 1,
                    color = 'rgba(102, 102, 102)'
                    ),
            colorscale = 'Blues',
            cmin = 0,
            colorbar_title = "Costco Store Locations" 
            )))

fig.update_layout(
            title = 'Python - Spatial Chart to show Costco Locations',
            geo = dict(
                    scope = 'usa',
                    projection_type = 'albers usa',
                    showland = True,
                    landcolor = "rgb(250, 250, 250)",
                    subunitcolor = "rgb(217, 217, 217)",
                    countrycolor = "rgb(217, 217, 217)",
                    countrywidth = 0.5,
                    subunitwidth = 0.5
                    ),
            )

fig.show()

Python - Contour Chart

## Initialize the matplotlb figure
f, ax = plt.subplots(figsize=(10,6))

sns.kdeplot(x=costco_df.Longitude, y = costco_df.Latitude, 
               cmap = "Blues", shade = True, thresh=0)

## Add chart title and labels
plt.title("Python - Contour Plot of Costco Location by Latitude and Logitude",
            fontsize = 15,loc = 'left')
plt.xlabel('Logtitude',fontsize =12)
plt.ylabel('Latitude', fontsize = 12)

plt.show()